R version 4.0.3 (2020-10-10) – “Bunny-Wunnies Freak Out”
Packages used for NMDS: vegan (version 2.5-7)
The document shows a series of NMDS ordinations for reference Coastal benthic communities in Virginia with environmental characteristics overlaid to evaluate natural differences in community compositions across coastal regions in Virginia. These NMDS will support the Genus level IBI development process. No West Virginia DEP data is used in this analysis.Reference sites were evaluated by regional biologists.
The dataset used includes all coastal reference stations collected in Virginia that were deemed reference through a series or water quality parameter filters and regional biologist review. Coastal stations were considered to be located in the Southeastern Plains and MidAtlantic Coastal Plains ecoregion. Communities were deemed to have different biological communities based on ordinations conducted on the entire reference dataset as is demonstrated in the document titled “NMDS for Reference Streams”. If stations appeared in the dataset more than 4 times, then the most recent 4 samples were used and the rest removed. Samples that had a total number of taxa below 100 collected at the time of sampling were also removed. Taxa that occurred in the dataset <= 5% of the time were removed. The data was log10 +1 transformed. Environmental factors were compiled for each station and used to plot over the NMDS to show environmental variation associated with the community matrix. The envfit function in Vegan was used to plot the continuous environmental variables. Some environmental variables like precipitation, slope, and elevation have not been calculated for all watersheds yet and will be added at a later date.
The first step was to read in the reference site bug taxa list and environmental factors dataset for each station. Join the environmental dataset with the bug dataset to account for multiple observations of each station and collection date and time.
Check to make sure the bug and environmental join was successful:
Number of rows in Community Matrix: 841
Number or rows in Environmental Matrix: 159
The data was log10+1 transformed. Rare taxa (<=5%) were removed.
## Run 0 stress 0.1929506
## Run 1 stress 0.1929503
## ... New best solution
## ... Procrustes: rmse 0.0002898364 max resid 0.001437352
## ... Similar to previous best
## Run 2 stress 0.193408
## ... Procrustes: rmse 0.009853025 max resid 0.07245202
## Run 3 stress 0.1929723
## ... Procrustes: rmse 0.001591056 max resid 0.01249925
## Run 4 stress 0.1929533
## ... Procrustes: rmse 0.001031338 max resid 0.00903929
## ... Similar to previous best
## Run 5 stress 0.1929528
## ... Procrustes: rmse 0.001991746 max resid 0.01676795
## Run 6 stress 0.193281
## ... Procrustes: rmse 0.007835778 max resid 0.04817411
## Run 7 stress 0.1929522
## ... Procrustes: rmse 0.0008807543 max resid 0.008535187
## ... Similar to previous best
## Run 8 stress 0.1930006
## ... Procrustes: rmse 0.004369923 max resid 0.03608604
## Run 9 stress 0.1929523
## ... Procrustes: rmse 0.0009493547 max resid 0.008720178
## ... Similar to previous best
## Run 10 stress 0.1956699
## Run 11 stress 0.1933834
## ... Procrustes: rmse 0.008778337 max resid 0.06966651
## Run 12 stress 0.1929525
## ... Procrustes: rmse 0.0009732866 max resid 0.008841471
## ... Similar to previous best
## Run 13 stress 0.1932825
## ... Procrustes: rmse 0.008118765 max resid 0.04952612
## Run 14 stress 0.1945606
## Run 15 stress 0.1929523
## ... Procrustes: rmse 0.00202229 max resid 0.01695859
## Run 16 stress 0.1948384
## Run 17 stress 0.1932324
## ... Procrustes: rmse 0.008255415 max resid 0.07136813
## Run 18 stress 0.193376
## ... Procrustes: rmse 0.01196463 max resid 0.08007374
## Run 19 stress 0.1934595
## Run 20 stress 0.1929739
## ... Procrustes: rmse 0.001925598 max resid 0.01561096
## *** Solution reached
##
## Call:
## metaMDS(comm = coastalFive[, 6:104], k = 3, trymax = 1000)
##
## global Multidimensional Scaling using monoMDS
##
## Data: coastalFive[, 6:104]
## Distance: bray
##
## Dimensions: 3
## Stress: 0.1929503
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'coastalFive[, 6:104]'
## NMDS1 NMDS2 r2 Pr(>r)
## Year 0.41437 0.91011 0.0149 0.87
## JulianDate -0.21420 0.97679 0.4497 0.01 **
## Latitude 0.87789 -0.47886 0.3141 0.01 **
## Longitude -0.56823 -0.82287 0.0612 0.54
## totalArea_sqMile 0.76770 0.64082 0.2421 0.04 *
## ELEVMEAN 0.70441 0.70979 0.2792 0.03 *
## SLPMEAN 0.99226 -0.12418 0.4272 0.01 **
## wshdRain_mmyr 0.76685 0.64183 0.2423 0.04 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 99
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$Season, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Fall Spring
## delta 0.6382 0.6449
## n 68 89
##
## Chance corrected within-group agreement A: 0.01845
## Based on observed delta 0.642 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$US_L3NAME, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Middle Atlantic Coastal Plain Southeastern Plains
## delta 0.6135 0.642
## n 25 132
##
## Chance corrected within-group agreement A: 0.02541
## Based on observed delta 0.6375 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$Basin_Code, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Appomattox Chowan-Dismal James-Lower Potomac-Lower Rappahannock
## delta 0.4982 0.6754 0.627 0.5909 0.5679
## n 3 29 32 6 22
## Small Coastal York
## delta 0.5892 0.636
## n 21 44
##
## Chance corrected within-group agreement A: 0.05018
## Based on observed delta 0.6213 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$ASSESS_REG, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## NRO PRO TRO
## delta 0.6474 0.6241 0.6072
## n 37 99 21
##
## Chance corrected within-group agreement A: 0.04091
## Based on observed delta 0.6273 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##Bioregion: Coastal
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$Bioregion, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Coast
## delta 0.6541
## n 157
##
## Chance corrected within-group agreement A: 0
## Based on observed delta 0.6541 and expected delta 0.6541
##
## Significance of delta: 1
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$Gradient, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## MACS Riffle
## delta 0.6522 0.5207
## n 149 8
##
## Chance corrected within-group agreement A: 0.0131
## Based on observed delta 0.6455 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$Order, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## 1 2 3 4 5 6
## delta 0.6135 0.6045 0.6182 0.6676 0.6042 0.3913
## n 33 42 33 33 14 2
##
## Chance corrected within-group agreement A: 0.05237
## Based on observed delta 0.6198 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$StreamCate, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## Large Medium Small
## delta 0.6628 0.6182 0.6337
## n 49 33 75
##
## Chance corrected within-group agreement A: 0.02227
## Based on observed delta 0.6395 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$WQS_CLASS, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## III VII
## delta 0.6527 0.6488
## n 116 41
##
## Chance corrected within-group agreement A: 0.0037
## Based on observed delta 0.6517 and expected delta 0.6541
##
## Significance of delta: 0.003
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$BioregionSeason, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## LargeCoastFall LargeCoastSpring MediumCoastFall MediumCoastSpring
## delta 0.6811 0.6218 0.6093 0.5903
## n 20 29 14 19
## SmallCoastFall SmallCoastSpring
## delta 0.6132 0.6341
## n 34 41
##
## Chance corrected within-group agreement A: 0.04326
## Based on observed delta 0.6258 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999
##
## Call:
## mrpp(dat = bugsnms_coast[, 6:104], grouping = samplescoresenv_coast$Bioregionsize, distance = "bray")
##
## Dissimilarity index: bray
## Weights for groups: n
##
## Class means and counts:
##
## LargeCoast MediumCoast SmallCoast
## delta 0.6628 0.6182 0.6337
## n 49 33 75
##
## Chance corrected within-group agreement A: 0.02227
## Based on observed delta 0.6395 and expected delta 0.6541
##
## Significance of delta: 0.001
## Permutation: free
## Number of permutations: 999